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An open source python library for automated feature engineering based on Genetic Programming

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Evolutionary Forest Documentation Status Updates

An open source python library for automated feature engineering based on Genetic Programming


Feature engineering is a long-standing issue that has plagued machine learning practitioners for many years. Deep learning techniques have significantly reduced the need for manual feature engineering in recent years. However, a critical issue is that the features discovered by deep learning methods are difficult to interpret.

In the domain of interpretable machine learning, genetic programming has demonstrated to be a promising method for automated feature construction, as it can improve the performance of traditional machine learning systems while maintaining similar interpretability. Nonetheless, such a potent method is rarely mentioned by practitioners. We believe that the main reason for this phenomenon is that there is still a lack of a mature package that can automatically build features based on the genetic programming algorithm. As a result, we propose this package with the goal of providing a powerful feature construction tool for enhancing existing state-of-the-art machine learning algorithms, particularly decision-tree based algorithms.


  • A powerful feature construction tool for generating interpretable machine learning features.

  • A reliable machine learning model has powerful performance on the small dataset.


From PyPI:

pip install -U evolutionary_forest

From GitHub (Latest Code):

pip install git+

Supported Algorithms


An example of usage:

X, y = load_diabetes(return_X_y=True)
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
r = EvolutionaryForestRegressor(max_height=3, normalize=True, select='AutomaticLexicase',
                                gene_num=10, boost_size=100, n_gen=20, n_pop=200, cross_pb=1,
                                base_learner='Random-DT', verbose=True), y_train)
print(r2_score(y_test, r.predict(x_test)))

An example of improvements brought about by constructed features:


Here are some nodebook examples of using Evolutionary Forest:


Tutorial: English Version | 中文版本


This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.


Please cite our paper if you find it helpful :)

  title={An Evolutionary Forest for Regression},
  author={Zhang, Hengzhe and Zhou, Aimin and Zhang, Hu},
  journal={IEEE Transactions on Evolutionary Computation},

  title={SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method},
  author={Zhang, Hengzhe and Zhou, Aimin and Chen, Qi and Xue, Bing and Zhang, Mengjie},
  journal={IEEE Transactions on Evolutionary Computation},


0.1.0 (2021-05-22)

  • First release on PyPI.

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